Lightweight Structured Line Map Based Visual Localization

Hongmin Liu, Chengyang Cao, Hanqiao Ye, Hainan Cui, Wei Gao, Xing Wang, Shuhan Shen

Abstract: Visual localization, also known as camera pose estimation, is a crucial component of many applications, such as robotics, autonomous driving, and augmented reality. Traditional visual localization algorithms typically run on point cloud maps generated by algorithms such as Structure-from-Motion (SfM) or Simultaneous Localization and Mapping (SLAM). However, point features are sensitive to weak textures and illumination changes. In addition, the generated 3D point cloud maps often contain millions of points, which puts higher demands on device storage and computing resources. To address these challenges, we propose a visual localization algorithm based on lightweight structured line maps. Instead of extracting and matching point features in the images, we select line segments that represent structured scene information as image features. These line segments are then used to construct a lightweight line map containing rich structured scene information. The camera pose is then estimated through a series of steps that include line extraction, matching, initial pose estimation, and pose refinement. Experimental results on benchmark datasets show that our method achieves competitive localization accuracy compared to current state-of-the-art visual localization methods, while significantly reducing the memory footprint of the 3D map.